Unemployment remains one of the most daunting challenges facing African nations today. It is a multifaceted problem with deep roots in socio-economic, educational, and policy-related factors. This case study invites analysts and policymakers to delve into various datasets to uncover insights and strategies that could assist in mitigating the unemployment crisis in Africa.
The primary goal of this case study is to analyze data, identify patterns, and propose informed, data-driven recommendations that governments and stakeholders can implement to effectively address and reduce unemployment rates, particularly focusing on the African context.
Participants will engage with six diverse datasets, each offering a unique perspective on factors influencing unemployment:
A significant spike in population growth is seen in nigeria from the 20000 and eventually surpassing the 200 million population.
Unemployment rate among Nigerians have been quiet low throughout the 90's as well as the early 20's. A spike was recorded from 2013 and it has been on the rise since.
Expenditure on education has been on the decline over the past 5 years and this is a worrying trend. Education plays a critical role in the development and growth of any nation and as seen in prior chart as population increases, expenditure in education should not be on the decrease but increase to cater to the needs of the growing population.
A steady increase in the percentage of the population with access to electricity is noted in the chart above. Although a desired 100% is still off, progress is gradually being recorded.
A steady increase in the number of registered LLC is seen in the country. The number have almost doubled over the course of the last decade going from around 800k to 1.6M from 2010 to 2020.
For a long period of time, Nigeria haven't operated a developed national strategy for youth employment. All these change in 2019 when the nation launched 2 strategy.
The heatmap above shows the relationship between all the indicators in the data. As expected there is a high positive correlation between unemployment in both male and female with population, number of LLC's and dusiness density ration. A mild correlation is noted between unemployment and electricity while a large negative correlation is seen between unemployment and government expendeiture on education. This goes on to show that as the amount spend on education increases, unemployment tends to decrease.
As expected, the data shows a negative relationship where unemployment reduces as the percentage of expenditure dedicated to education increases. This hold true for both male and female population.
The linear regression model above is represented by the equation
\begin{equation} y = 0.03247930391540247x_1 - 0.4969933374998647x_2 + 9.968270789033804 \end{equation}For every 1% increase in female unemployment, government expenditure on education is expected to increase by 0.0325%.
For every 1% increase in male unemployment, government expenditure on education is expected to decrease by 0.497%.
The intercept (9.97) represents the baseline education expenditure with zero unemployment.
The relationship shows a mild correlation between the two indicators.
Strong relationship noted between business density and total unemployment as expected.
Forecast shows that the value of
Total business density rateis expected to continue its upward trajectory. This trends needs to be supported and the slope made steeper if possible to provide more employment for the growing population.
South AfricaandDiboutitop the unemployment chart in Africa.
Policymakers should consider the following adjustments in response to the predictive model findings findings: